TM 745 Forecasting for Business & Technology Dr. Frank Joseph Matejcik

1. TM 745 Forecasting for Business & TechnologyDr. Frank Joseph Matejcik 3rd Session 2/11/08: Chapter 3 Moving Averages and Exponential Smoothing South Dakota School of Mines and Technology, Rapid City

2. Agenda & New Assignment • ch3(1,5,8,11) Tentative Schedule • Chapter 3 WK (with odd diversions) • Try to use ForecastX for Autocorrelation • Business Forecasting 5th Edition J. Holton Wilson & Barry KeatingMcGraw-Hill

3. Tentative Schedule Chapters Assigned 28-Jan 1 problems 1,4,8 e-mail, contact 4-Feb 2 problems 4, 8, 9 11-Feb 3 problems 1,5,8,11 18-Feb President’s Day 25-Feb 4 problems 6,10 3-Mar 5 problems 5,8 10-Mar Exam 1 Ch 1-4 Revised 17-Mar Break 24-Mar Easter 31-Mar 6 problems 4, 7 Chapters Assigned 7-Apr 7 3,4,5(series A) 7B 21-Apr 8 Problem 6 28-Apr 9 05-May Final

4. Web Resources • Class Web site on the HPCnet system • http://sdmines.sdsmt.edu/sdsmt/directory/courses/2008sp/tm745M021 • Streaming video http://its.sdsmt.edu/Distance/ • Answers will be online. Linked from ^ • The same class session that is on the DVD is on the stream in lower quality. http://www.flashget.com/ will allow you to capture the stream more readily and review the lecture, anywhere you can get your computer to run.

5. Moving Averages & Exponential Smoothing • All basic methods based on smoothing • 1. Moving averages • 2. Simple exponential smoothing • 3. Holt's exponential smoothing • 4. Winters' exponential smoothing • 5. Adaptive-response-rate single exponential smoothing

6. Moving Averages • Ex. “Three Quarter Moving Average”(1999Q1+1999Q2+1999Q3)/3 =Forecast for 1999Q4 • Slutsky-Yule effect: Any moving average could appear to be acycle, because it is a serially correlated set of random numbers.

9. Simple Exponential Smoothing

10. Simple Exponential Smoothing • Alternative interpretation

11. Simple Exponential Smoothing • Why they call it exponential property

13. Simple Exponential Smoothing • Advantages • Simpler than other forms • Requires limited data • Disdvantages • Lags behind actual data • No trend or seasonality

14. Holt's Exponential Smoothing(Double Holt in ForecastXTM)

15. ForecastXTM Conventions forSmoothing Constants • Alpha (a) =the simple smoothing constant • Gamma (g) =the trend smoothing constant • Beta (b) =the seasonality smoothing constant

18. Holt's Exponential Smoothing • ForecastX will pick the smoothing constants to minimize RMSE • Some trend, but no seasonality • Call it linear trend smoothing

19. Winters'

21. Adaptive-Response-Rate Single Exponential Smoothing

24. Adaptive-Response-Rate Single Exponential Smoothing • Adaptive is a clue to how it works • No direct way of handling seasonality • Does not handle trends • ForecastX has different algorithm

25. Using Single, Holt's, or ADRES Smoothing to Forecast a Seasonal Data Series • 1. Calculate seasonal indices for the series. Done in HOLT WINTERS ForecastX™. • 2. Deseasonalize the original data by dividing each value by its corresponding seasonal index.

26. Using Single, Holt's, or ADRES Smoothing to Forecast a Seasonal Data Series • 3. Apply a forecasting method (such as ES, Holt's, or ADRES) to the deseasonalized series to produce an intermediate forecast of the deseasonalized data. • 4. Reseasonalize the series by multiplying each deseasonalized forecast by its corresponding seasonal index.

28. New-Product Forecasting(growth curve fitting)

29. Gompertz Curve

33. Logistic Curve

36. Bass Model (See Chapter 1,too)

41. Event Modeling • Event Indices Legend 0. No event present • Free-standing inserts (FSIs) • FSI/radio, television, print campaign • Load (trade promotion) • Deload (month after effect of load) • Thematics (themed adg campaign) • Instant redeemable coupon (IRC)

44. Forecasting Jewelry Sales using Exponential Smoothing

45. Forecasting Jewelry Sales using Exponential Smoothing

46. Forecasting Houses Sold Sales using Exponential Smoothing

47. Forecasting Houses Sold Sales using Exponential Smoothing

48. Summary • All basic methods based on smoothing • 1. Moving averages • 2. Simple exponential smoothing • 3. Holt's exponential smoothing • 4. Winters' exponential smoothing • 5. Adaptive-response-rate single exponential smoothing • Use of Deseasonalized Series • techniques not clear winners

49. Integrative Case: The Gap

50. Solutions toCase Questions #1